BackgroundSince their introduction in 2009, the BioNLP Shared Task events have been instrumental in advancing the development of methods and resources for the automatic extraction of information from the biomedical literature. In this paper, we present the Cancer Genetics (CG) and Pathway Curation (PC) tasks, two event extraction tasks introduced in the BioNLP Shared Task 2013. The CG task focuses on cancer, emphasizing the extraction of physiological and pathological processes at various levels of biological organization, and the PC task targets reactions relevant to the development of biomolecular pathway models, defining its extraction targets on the basis of established pathway representations and ontologies.ResultsSix groups participated in the CG task and two groups in the PC task, together applying a wide range of extraction approaches including both established state-of-the-art systems and newly introduced extraction methods. The best-performing systems achieved F-scores of 55% on the CG task and 53% on the PC task, demonstrating a level of performance comparable to the best results achieved in similar previously proposed tasks.ConclusionsThe results indicate that existing event extraction technology can generalize to meet the novel challenges represented by the CG and PC task settings, suggesting that extraction methods are capable of supporting the construction of knowledge bases on the molecular mechanisms of cancer and the curation of biomolecular pathway models. The CG and PC tasks continue as open challenges for all interested parties, with data, tools and resources available from the shared task homepage.

BackgroundThyroid incidentalomas detected by 2-deoxy-2-18F-fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) have been reported in 1% to 4% of the population, with a risk of malignancy of 27.8% to 74%. We performed a retrospective review of FDG-avid thyroid incidentalomas in cancer screening subjects and patients with nonthyroid cancer. The risk of malignancy in thyroid incidentaloma and its association with the maximal standardized uptake value (SUVmax) in 18F-FDG PET/CT were evaluated to define the predictor variables in assessing risk of malignancy.MethodsA total of 2,584 subjects underwent 18F-FDG PET/CT for metastatic evaluation or cancer screening from January 2005 to January 2010. Among them, 36 subjects with FDG-avid thyroid incidentalomas underwent further diagnostic evaluation (thyroid ultrasonography-guided fine needle aspiration cytology [FNAC] or surgical resection). We retrospectively reviewed the database of these subjects.ResultsOf the 2,584 subjects who underwent 18F-FDG PET/CT (319 for cancer screening and 2,265 for metastatic evaluation), 52 (2.0%) were identified as having FDG-avid thyroid incidentaloma and cytologic diagnosis was obtained by FNAC in 36 subjects. Of the subjects, 15 were proven to have malignant disease: 13 by FNAC and two by surgical resection. The positive predictive value of malignancy in FDG-avid thyroid incidentaloma was 41.7%. Median SUVmax was higher in malignancy than in benign lesions (4.7 [interquartile range (IQR), 3.4 to 6.0] vs. 2.8 [IQR, 2.6 to 4.0], P=0.001).ConclusionThyroid incidentalomas found on 18F-FDG PET/CT have a high risk of malignancy, with a positive predictive value of 41.7%. FDG-avid thyroid incidentalomas with higher SUVmax tended to be malignant.

BackgroundBioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research.ResultsThe theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces. We discussed on topics including guidelines for designing semantic data and interoperability of resources. We consequently developed tools and clients for analysis and visualization.ConclusionWe provide a meeting report from BioHackathon 2010, in which we describe the discussions, decisions, and breakthroughs made as we moved towards compliance with Semantic Web technologies - from source provider, through middleware, to the end-consumer.

Globally, one of the biggest problems with the increase in the elderly population is dementia. However, dementia still has no fundamental cure. Therefore, it is important to predict and prevent dementia early. For early prediction of dementia, it is crucial to find dementia risk factors that increase a person’s risk of developing dementia. In this paper, the subject of dementia risk factor analysis and discovery studies were limited to gender, because it is assumed that the difference in the prevalence of dementia in men and women will lead to differences in the risk factors for dementia among men and women. This study analyzed the Korean National Health Information System—Senior Cohort using machine-learning techniques. By using the machine-learning technique, it was possible to reveal a very small causal relationship between data that are ignored using existing statistical techniques. By using the senior cohort, it was possible to analyze 6000 data that matched the experimental conditions out of 558,147 sample subjects over 14 years. In order to analyze the difference in dementia risk factors between men and women, three machine-learning-based dementia risk factor analysis models were constructed and compared. As a result of the experiment, it was found that the risk factors for dementia in men and women are different. In addition, not only did the results include most of the known dementia risk factors, previously unknown candidates for dementia risk factors were also identified. We hope that our research will be helpful in finding new dementia risk factors.